BuildStrategy api comment (#17348)

Python examples of fluid.layers.io.double_buffer and some BuildStrategy's methods.

paddle/fluid/API.spec

@@ -242,7 +242,7 @@ paddle.fluid.layers.open_files (ArgSpec(args=['filenames', 'shapes', 'lod_levels
 paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', '32181f6037e387fb6e68a5beaafe33b6'))
 paddle.fluid.layers.shuffle (ArgSpec(args=['reader', 'buffer_size'], varargs=None, keywords=None, defaults=None), ('document', 'f967a73426db26f970bc70bfb03cffca'))
 paddle.fluid.layers.batch (ArgSpec(args=['reader', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', 'fcb24383c6eef2ca040ee824c26e22fd'))
-paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '07e5b796674796eb1ef3fee9c10d24e3'))
+paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'c13b8a8521bea5f8123b925ae2a5d5db'))
 paddle.fluid.layers.random_data_generator (ArgSpec(args=['low', 'high', 'shapes', 'lod_levels', 'for_parallel'], varargs=None, keywords=None, defaults=(True,)), ('document', '9b7f0f86ec24bbc97643cadcb6499cff'))
 paddle.fluid.layers.py_reader (ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True)), ('document', '5c54493d96c7e0760dc6758af1c8dd72'))
 paddle.fluid.layers.create_py_reader_by_data (ArgSpec(args=['capacity', 'feed_list', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, True)), ('document', 'b42332b894e1e0962c6a43f0151c2640'))

paddle/fluid/pybind/pybind.cc

@@ -1334,14 +1334,9 @@ All parameter, weight, gradient are variables in Paddle.
     Examples:
         .. code-block:: python
 
-          build_strategy = fluid.BuildStrategy()
-          build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
-
-          train_exe = fluid.ParallelExecutor(use_cuda=True,
-                                             loss_name=loss.name,
-                                             build_strategy=build_strategy)
-
-          train_loss, = train_exe.run([loss.name], feed=feed_dict)
+            import paddle.fluid as fluid
+            build_strategy = fluid.BuildStrategy()
+            build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
 )DOC");
 
   py::enum_<BuildStrategy::ReduceStrategy>(build_strategy, "ReduceStrategy")
@@ -1363,11 +1358,19 @@ All parameter, weight, gradient are variables in Paddle.
             self.reduce_ = strategy;
           },
           R"DOC(The type is STR, there are two reduce strategies in ParallelExecutor,
-                  'AllReduce' and 'Reduce'. If you want that all the parameters'
-                  optimization are done on all devices independently, you should choose 'AllReduce';
-                  if you choose 'Reduce', all the parameters' optimization will be evenly distributed
-                  to different devices, and then broadcast the optimized parameter to other devices.
-                  In some models, `Reduce` is faster. Default 'AllReduce'. )DOC")
+                'AllReduce' and 'Reduce'. If you want that all the parameters'
+                optimization are done on all devices independently, you should choose 'AllReduce';
+                if you choose 'Reduce', all the parameters' optimization will be evenly distributed
+                to different devices, and then broadcast the optimized parameter to other devices.
+                In some models, `Reduce` is faster. Default 'AllReduce'.
+
+                Examples:
+                    .. code-block:: python
+
+                        import paddle.fluid as fluid
+                        build_strategy = fluid.BuildStrategy()
+                        build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
+                  )DOC")
       .def_property(
           "gradient_scale_strategy",
           [](const BuildStrategy &self) { return self.gradient_scale_; },
@@ -1377,10 +1380,18 @@ All parameter, weight, gradient are variables in Paddle.
             self.gradient_scale_ = strategy;
           },
           R"DOC(The type is STR, there are three ways of defining :math:`loss@grad` in
-                   ParallelExecutor, 'CoeffNumDevice', 'One' and 'Customized'. By default,
-                   ParallelExecutor sets the :math:`loss@grad` according to the number of devices.
-                   If you want to customize :math:`loss@grad`, you can choose 'Customized'.
-                   Default 'CoeffNumDevice'.)DOC")
+                ParallelExecutor, 'CoeffNumDevice', 'One' and 'Customized'. By default,
+                ParallelExecutor sets the :math:`loss@grad` according to the number of devices.
+                If you want to customize :math:`loss@grad`, you can choose 'Customized'.
+                Default 'CoeffNumDevice'.
+
+                Examples:
+                    .. code-block:: python
+
+                        import paddle.fluid as fluid
+                        build_strategy = fluid.BuildStrategy()
+                        build_strategy.gradient_scale_strategy = True
+                   )DOC")
       .def_property(
           "debug_graphviz_path",
           [](const BuildStrategy &self) { return self.debug_graphviz_path_; },
@@ -1389,8 +1400,16 @@ All parameter, weight, gradient are variables in Paddle.
             self.debug_graphviz_path_ = path;
           },
           R"DOC(The type is STR, debug_graphviz_path indicate the path that
-                    writing the SSA Graph to file in the form of graphviz, you.
-                    It is useful for debugging. Default "")DOC")
+                writing the SSA Graph to file in the form of graphviz.
+                It is useful for debugging. Default ""
+
+                Examples:
+                    .. code-block:: python
+
+                        import paddle.fluid as fluid
+                        build_strategy = fluid.BuildStrategy()
+                        build_strategy.debug_graphviz_path = ""
+                    )DOC")
       .def_property(
           "enable_sequential_execution",
           [](const BuildStrategy &self) {
@@ -1400,7 +1419,15 @@ All parameter, weight, gradient are variables in Paddle.
             PADDLE_ENFORCE(!self.IsFinalized(), "BuildStrategy is finlaized.");
             self.enable_sequential_execution_ = b;
           },
-          R"DOC(The type is BOOL. If set True, the execution order of ops would be the same as what is in the program. Default False.)DOC")
+          R"DOC(The type is BOOL. If set True, the execution order of ops would be the same as what is in the program. Default False.
+
+                Examples:
+                    .. code-block:: python
+
+                        import paddle.fluid as fluid
+                        build_strategy = fluid.BuildStrategy()
+                        build_strategy.enable_sequential_execution = True
+          )DOC")
       .def_property(
           "remove_unnecessary_lock",
           [](const BuildStrategy &self) {
@@ -1410,7 +1437,15 @@ All parameter, weight, gradient are variables in Paddle.
             PADDLE_ENFORCE(!self.IsFinalized(), "BuildStrategy is finlaized.");
             self.remove_unnecessary_lock_ = b;
           },
-          R"DOC(The type is BOOL. If set True, some locks in GPU ops would be released and ParallelExecutor would run faster. Default True.)DOC")
+          R"DOC(The type is BOOL. If set True, some locks in GPU ops would be released and ParallelExecutor would run faster. Default True.
+
+                Examples:
+                    .. code-block:: python
+
+                        import paddle.fluid as fluid
+                        build_strategy = fluid.BuildStrategy()
+                        build_strategy.remove_unnecessary_lock = True
+          )DOC")
       .def_property(
           "num_trainers",
           [](const BuildStrategy &self) { return self.num_trainers_; },
@@ -1439,8 +1474,16 @@ All parameter, weight, gradient are variables in Paddle.
             self.fuse_elewise_add_act_ops_ = b;
           },
           R"DOC(The type is BOOL, fuse_elewise_add_act_ops indicate whether
-                     to fuse elementwise_add_op and activation_op,
-                     it may make the execution faster. Default False)DOC")
+                to fuse elementwise_add_op and activation_op,
+                it may make the execution faster. Default False
+
+                Examples:
+                    .. code-block:: python
+
+                        import paddle.fluid as fluid
+                        build_strategy = fluid.BuildStrategy()
+                        build_strategy.fuse_elewise_add_act_ops = True
+                     )DOC")
       .def_property(
           "fuse_relu_depthwise_conv",
           [](const BuildStrategy &self) {
@@ -1451,10 +1494,18 @@ All parameter, weight, gradient are variables in Paddle.
             self.fuse_relu_depthwise_conv_ = b;
           },
           R"DOC(The type is BOOL, fuse_relu_depthwise_conv indicate whether
-                      to fuse relu and depthwise_conv2d,
-                      it will save GPU memory and may make the execution faster.
-                      This options is only available in GPU devices.
-                      Default False.)DOC")
+                to fuse relu and depthwise_conv2d,
+                it will save GPU memory and may make the execution faster.
+                This options is only available in GPU devices.
+                Default False.
+
+                Examples:
+                    .. code-block:: python
+
+                        import paddle.fluid as fluid
+                        build_strategy = fluid.BuildStrategy()
+                        build_strategy.fuse_relu_depthwise_conv = True
+          )DOC")
       .def_property(
           "fuse_broadcast_ops",
           [](const BuildStrategy &self) { return self.fuse_broadcast_ops_; },
@@ -1491,7 +1542,15 @@ All parameter, weight, gradient are variables in Paddle.
                 Current implementation doesn't support FP16 training and CPU.
                 And only synchronous on one machine, not all machines.
 
-                Default False)DOC")
+                Default False
+
+                Examples:
+                    .. code-block:: python
+
+                        import paddle.fluid as fluid
+                        build_strategy = fluid.BuildStrategy()
+                        build_strategy.sync_batch_norm = True
+                )DOC")
       .def_property(
           "memory_optimize",
           [](const BuildStrategy &self) { return self.memory_optimize_; },

python/paddle/fluid/layers/io.py

@@ -1055,7 +1055,8 @@ def double_buffer(reader, place=None, name=None):
 
     Examples:
 
-        >>> reader = fluid.layers.open_files(filenames=['somefile'],
+        >>> import paddle.fluid as fluid
+        >>> reader = fluid.layers.open_files(filenames=['mnist.recordio'],
         >>>                                  shapes=[[-1, 784], [-1, 1]],
         >>>                                  dtypes=['float32', 'int64'])
         >>> reader = fluid.layers.double_buffer(reader)